Hi Cheolsoo, Just ran the benchmarks: no luck.
No combiner + mapPartAgg set to true is slower than without the combiner: real 752.85 real 757.41 real 749.03 On 25 August 2013 17:11, Benjamin Jakobus <[email protected]> wrote: > Hi Cheolsoo, > > Thanks - let's see, I'll give it a try now. > > Best Regards, > Ben > > > On 25 August 2013 02:27, Cheolsoo Park <[email protected]> wrote: > >> Hi Benjamin, >> >> Thanks for letting us know. That means my original assumption was wrong. >> The size of bags is not small. In fact, you can compute the avg size of >> bags as follows: total number of input records / ( reduce input groups x >> number of reducers ). >> >> One more thing you can try is turning on "pig.exec.mapPartAgg". That may >> help mappers run faster. If this doesn't work, I run out of ideas. :-) >> >> Thanks, >> Cheolsoo >> >> >> >> On Sat, Aug 24, 2013 at 3:27 AM, Benjamin Jakobus <[email protected] >> >wrote: >> >> > Hi Alan, Cheolsoo, >> > >> > I re-ran the benchmarks with and without the combiner. Enabling the >> > combiner is faster: >> > >> > With combiner: >> > real 668.44 >> > real 663.10 >> > real 665.05 >> > >> > Without combiner: >> > real 795.97 >> > real 810.51 >> > real 810.16 >> > >> > Best Regards, >> > Ben >> > >> > >> > On 22 August 2013 16:33, Cheolsoo Park <[email protected]> wrote: >> > >> > > Hi Benjamin, >> > > >> > > To answer your question, how the Hadoop combiner works is that 1) >> mappers >> > > write outputs to disk and 2) combiners read them, combine and write >> them >> > > again. So you're paying extra disk I/O as well as >> > > serialization/deserialization. >> > > >> > > This will pay off if combiners significantly reduce the intermediate >> > > outputs that reducers need to fetch from mappers. But if reduction is >> not >> > > significant, it will only slow down mappers. You can identify whether >> > this >> > > is really a problem by comparing the time spent by map and combine >> > > functions in the task logs. >> > > >> > > What I usually do are: >> > > 1) If there are many small bags, disable combiners. >> > > 2) If there are many large bags, enable combiners. Furthermore, >> turning >> > on >> > > "pig.exec.mapPartAgg" helps. (see the Pig >> > > blog<https://blogs.apache.org/pig/entry/apache_pig_it_goes_to>for >> > > details. >> > > ) >> > > >> > > Thanks, >> > > Cheolsoo >> > > >> > > >> > > On Thu, Aug 22, 2013 at 4:01 AM, Benjamin Jakobus < >> > [email protected] >> > > >wrote: >> > > >> > > > Hi Cheolsoo, >> > > > >> > > > Thanks - I will try this now and get back to you. >> > > > >> > > > Out of interest; could you explain (or point me towards resources >> that >> > > > would) why the combiner would be a problem? >> > > > >> > > > Also, could the fact that Pig builds an intermediary data structure >> (?) >> > > > whilst Hive just performs a sort then the arithmetic operation >> explain >> > > the >> > > > slowdown? >> > > > >> > > > (Apologies, I'm quite new to Pig/Hive - just my guesses). >> > > > >> > > > Regards, >> > > > Benjamin >> > > > >> > > > >> > > > On 22 August 2013 01:07, Cheolsoo Park <[email protected]> >> wrote: >> > > > >> > > > > Hi Benjamin, >> > > > > >> > > > > Thank you very much for sharing detailed information! >> > > > > >> > > > > 1) From the runtime numbers that you provided, the mappers are >> very >> > > slow. >> > > > > >> > > > > CPU time spent (ms)5,081,610168,7405,250,350CPU time spent >> > > (ms)5,052,700 >> > > > > 178,2205,230,920CPU time spent (ms)5,084,430193,4805,277,910 >> > > > > >> > > > > 2) In your GROUP BY query, you have an algebraic UDF "COUNT". >> > > > > >> > > > > I am wondering whether disabling combiner will help here. I have >> > seen a >> > > > lot >> > > > > of cases where combiner actually hurt performance significantly >> if it >> > > > > doesn't combine mapper outputs significantly. Briefly looking at >> > > > > generate_data.pl in PIG-200, it looks like a lot of random keys >> are >> > > > > generated. So I guess you will end up with a large number of small >> > bags >> > > > > rather than a small number of large bags. If that's the case, >> > combiner >> > > > will >> > > > > only add overhead to mappers. >> > > > > >> > > > > Can you try to include this "set pig.exec.nocombiner true;" and >> see >> > > > whether >> > > > > it helps? >> > > > > >> > > > > Thanks, >> > > > > Cheolsoo >> > > > > >> > > > > >> > > > > >> > > > > >> > > > > >> > > > > >> > > > > On Wed, Aug 21, 2013 at 3:52 AM, Benjamin Jakobus < >> > > > [email protected] >> > > > > >wrote: >> > > > > >> > > > > > Hi Cheolsoo, >> > > > > > >> > > > > > >>What's your query like? Can you share it? Do you call any >> > algebraic >> > > > UDF >> > > > > > >> after group by? I am wondering whether combiner matters in >> your >> > > > test. >> > > > > > I have been running 3 different types of queries. >> > > > > > >> > > > > > The first was performed on datasets of 6 different sizes: >> > > > > > >> > > > > > >> > > > > > - Dataset size 1: 30,000 records (772KB) >> > > > > > - Dataset size 2: 300,000 records (6.4MB) >> > > > > > - Dataset size 3: 3,000,000 records (63MB) >> > > > > > - Dataset size 4: 30 million records (628MB) >> > > > > > - Dataset size 5: 300 million records (6.2GB) >> > > > > > - Dataset size 6: 3 billion records (62GB) >> > > > > > >> > > > > > The datasets scale linearly, whereby the size equates to 3000 * >> > 10n . >> > > > > > A seventh dataset consisting of 1,000 records (23KB) was >> produced >> > to >> > > > > > perform join >> > > > > > operations on. Its schema is as follows: >> > > > > > name - string >> > > > > > marks - integer >> > > > > > gpa - float >> > > > > > The data was generated using the generate data.pl perl script >> > > > available >> > > > > > for >> > > > > > download >> > > > > > from https://issues.apache.org/jira/browse/PIG-200 to produce >> the >> > > > > > datasets. The results are as follows: >> > > > > > >> > > > > > >> > > > > > * * * * * * *Set 1 * *Set 2** * *Set >> 3** >> > > > * >> > > > > > *Set >> > > > > > 4** * *Set 5** * *Set 6* >> > > > > > *Arithmetic** * 32.82* * 36.21* * 49.49* * >> > 83.25* >> > > > > > * >> > > > > > 423.63* * 3900.78 >> > > > > > *Filter 10%** * 32.94* * 34.32* * 44.56* * >> > 66.68* >> > > > > > * >> > > > > > 295.59* * 2640.52 >> > > > > > *Filter 90%** * 33.93* * 32.55* * 37.86* * >> > 53.22* >> > > > > > * >> > > > > > 197.36* * 1657.37 >> > > > > > *Group** * * *49.43* * 53.34* * 69.84* >> * >> > > > 105.12* >> > > > > > *497.61* * 4394.21 >> > > > > > *Join** * * * 49.89* * 50.08* * 78.55* >> * >> > > > > 150.39* >> > > > > > *1045.34* *10258.19 >> > > > > > *Averaged performance of arithmetic, join, group, order, >> distinct >> > > > select >> > > > > > and filter operations on six datasets using Pig. Scripts were >> > > > configured >> > > > > as >> > > > > > to use 8 reduce and 11 map tasks.* >> > > > > > >> > > > > > >> > > > > > >> > > > > > * * * Set 1** * *Set 2** * *Set 3** >> > > * >> > > > > > *Set >> > > > > > 4** * *Set 5** * *Set 6* >> > > > > > *Arithmetic** * 32.84* * 37.33* * 72.55* * >> > > 300.08 >> > > > > > 2633.72 27821.19 >> > > > > > *Filter 10% * 32.36* * 53.28* * 59.22* * >> > 209.5* >> > > > > * >> > > > > > 1672.3* *18222.19 >> > > > > > *Filter 90% * 31.23* * 32.68* * 36.8* * >> > 69.55* >> > > > > > * >> > > > > > 331.88* *3320.59 >> > > > > > *Group * * * 48.27* * 47.68* * 46.87* * >> > > 53.66* >> > > > > > *141.36* *1233.4 >> > > > > > *Join * * * * *48.54* *56.86* * 104.6* >> * >> > > > > 517.5* >> > > > > > * 4388.34* * - >> > > > > > *Distinct** * * *48.73* *53.28* * 72.54* >> * >> > > > > 109.77* >> > > > > > * - * * * * - >> > > > > > *Averaged performance of arithmetic, join, group, distinct >> select >> > and >> > > > > > filter operations on six datasets using Hive. Scripts were >> > configured >> > > > as >> > > > > to >> > > > > > use 8 reduce and 11 map tasks.* >> > > > > > >> > > > > > (If you want to see the standard deviation, let me know). >> > > > > > >> > > > > > So, to summarize the results: Pig outperforms Hive, with the >> > > exception >> > > > of >> > > > > > using *Group By*. >> > > > > > >> > > > > > The Pig scripts used for this benchmark are as follows: >> > > > > > *Arithmetic* >> > > > > > -- Generate with basic arithmetic >> > > > > > A = load '$input/dataset_300000000' using PigStorage('\t') as >> > (name, >> > > > age, >> > > > > > gpa) PARALLEL $reducers; >> > > > > > B = foreach A generate age * gpa + 3, age/gpa - 1.5 PARALLEL >> > > $reducers; >> > > > > > store B into '$output/dataset_300000000_projection' using >> > > PigStorage() >> > > > > > PARALLEL $reducers; >> > > > > > >> > > > > > * >> > > > > > * >> > > > > > *Filter 10%* >> > > > > > -- Filter that removes 10% of data >> > > > > > A = load '$input/dataset_300000000' using PigStorage('\t') as >> > (name, >> > > > age, >> > > > > > gpa) PARALLEL $reducers; >> > > > > > B = filter A by gpa < '3.6' PARALLEL $reducers; >> > > > > > store B into '$output/dataset_300000000_filter_10' using >> > PigStorage() >> > > > > > PARALLEL $reducers; >> > > > > > >> > > > > > >> > > > > > *Filter 90%* >> > > > > > -- Filter that removes 90% of data >> > > > > > A = load '$input/dataset_300000000' using PigStorage('\t') as >> > (name, >> > > > age, >> > > > > > gpa) PARALLEL $reducers; >> > > > > > B = filter A by age < '25' PARALLEL $reducers; >> > > > > > store B into '$output/dataset_300000000_filter_90' using >> > PigStorage() >> > > > > > PARALLEL $reducers; >> > > > > > >> > > > > > * >> > > > > > * >> > > > > > *Group* >> > > > > > A = load '$input/dataset_300000000' using PigStorage('\t') as >> > (name, >> > > > age, >> > > > > > gpa) PARALLEL $reducers; >> > > > > > B = group A by name PARALLEL $reducers; >> > > > > > C = foreach B generate flatten(group), COUNT(A.age) PARALLEL >> > > $reducers; >> > > > > > store C into '$output/dataset_300000000_group' using >> PigStorage() >> > > > > PARALLEL >> > > > > > $reducers; >> > > > > > * >> > > > > > * >> > > > > > *Join* >> > > > > > A = load '$input/dataset_300000000' using PigStorage('\t') as >> > (name, >> > > > age, >> > > > > > gpa) PARALLEL $reducers; >> > > > > > B = load '$input/dataset_join' using PigStorage('\t') as (name, >> > age, >> > > > gpa) >> > > > > > PARALLEL $reducers; >> > > > > > C = cogroup A by name inner, B by name inner PARALLEL $reducers; >> > > > > > D = foreach C generate flatten(A), flatten(B) PARALLEL >> $reducers; >> > > > > > store D into '$output/dataset_300000000_cogroup_big' using >> > > PigStorage() >> > > > > > PARALLEL $reducers; >> > > > > > >> > > > > > Similarly, here the Hive scripts: >> > > > > > *Arithmetic* >> > > > > > SELECT (dataset.age * dataset.gpa + 3) AS F1, >> > > (dataset.age/dataset.gpa >> > > > - >> > > > > > 1.5) AS F2 >> > > > > > FROM dataset >> > > > > > WHERE dataset.gpa > 0; >> > > > > > >> > > > > > *Filter 10%* >> > > > > > SELECT * >> > > > > > FROM dataset >> > > > > > WHERE dataset.gpa < 3.6; >> > > > > > >> > > > > > *Filter 90%* >> > > > > > SELECT * >> > > > > > FROM dataset >> > > > > > WHERE dataset.age < 25; >> > > > > > >> > > > > > *Group* >> > > > > > SELECT COUNT(dataset.age) >> > > > > > FROM dataset >> > > > > > GROUP BY dataset.name; >> > > > > > >> > > > > > *Join* >> > > > > > SELECT * >> > > > > > FROM dataset JOIN dataset_join >> > > > > > ON dataset.name = dataset_join.name; >> > > > > > >> > > > > > I will re-run the benchmarks to see whether it is the reduce or >> map >> > > > side >> > > > > > that is slower and get back to you later today. >> > > > > > >> > > > > > The other two benchmarks were slightly different: I performed >> > > > transitive >> > > > > > self joins in which Pig outperformed Hive. However once I added >> a >> > > Group >> > > > > By, >> > > > > > Hive began outperforming Pig. >> > > > > > >> > > > > > I also ran the TPC-H benchmarks and noticed that Hive >> > (surprisingly) >> > > > > > outperformed Pig. However what *seems* to cause the actual >> > > performance >> > > > > > difference is the heavy usage of the Group By operator in all >> but 3 >> > > > TPC-H >> > > > > > test scripts. >> > > > > > >> > > > > > Re-running the scripts whilst omitting the the grouping of data >> > > > produces >> > > > > > the expected results. For example, running script 3 >> > > > > > (q3_shipping_priority.pig) whilst omitting the Group By operator >> > > > > > significantly reduces the runtime (to 1278.49 seconds real time >> > > runtime >> > > > > or >> > > > > > a total of 12,257,630ms CPU time). >> > > > > > >> > > > > > The fact that the Group By operator skews the TPC-H benchmark in >> > > favour >> > > > > of >> > > > > > Apache Hive is supported by further experiments: as noted >> earlier a >> > > > > > benchmark was carried out on a transitive self-join. The former >> > took >> > > > Pig >> > > > > an >> > > > > > average of 45.36 seconds (real time runtime) to execute; it took >> > Hive >> > > > > 56.73 >> > > > > > seconds. The latter took Pig 157.97 and Hive 180.19 seconds >> > (again, >> > > on >> > > > > > average). However adding the Group By operator to the scripts >> > turned >> > > > the >> > > > > > tides: Pig is now significantly slower than Hive, requiring an >> > > average >> > > > of >> > > > > > 278.15 seconds. Hive on the other hand required only 204.01 to >> > > perform >> > > > > the >> > > > > > JOIN and GROUP operations. >> > > > > > >> > > > > > Real time runtime is measured using the time -p command. >> > > > > > >> > > > > > Best Regards, >> > > > > > Benjamin >> > > > > > >> > > > > > >> > > > > > >> > > > > > On 20 August 2013 19:56, Cheolsoo Park <[email protected]> >> > wrote: >> > > > > > >> > > > > > > Hi Benjarmin, >> > > > > > > >> > > > > > > Can you describe which step of group by is slow? Mapper side >> or >> > > > reducer >> > > > > > > side? >> > > > > > > >> > > > > > > What's your query like? Can you share it? Do you call any >> > algebraic >> > > > UDF >> > > > > > > after group by? I am wondering whether combiner matters in >> your >> > > test. >> > > > > > > >> > > > > > > Thanks, >> > > > > > > Cheolsoo >> > > > > > > >> > > > > > > >> > > > > > > >> > > > > > > >> > > > > > > On Tue, Aug 20, 2013 at 2:27 AM, Benjamin Jakobus < >> > > > > > [email protected] >> > > > > > > >wrote: >> > > > > > > >> > > > > > > > Hi all, >> > > > > > > > >> > > > > > > > After benchmarking Hive and Pig, I found that the Group By >> > > operator >> > > > > in >> > > > > > > Pig >> > > > > > > > is drastically slower that Hive's. I was wondering whether >> > > anybody >> > > > > has >> > > > > > > > experienced the same? And whether people may have any tips >> for >> > > > > > improving >> > > > > > > > the performance of this operation? (Adding a DISTINCT as >> > > suggested >> > > > by >> > > > > > an >> > > > > > > > earlier post on here doesn't help. I am currently re-running >> > the >> > > > > > > benchmark >> > > > > > > > with LZO compression enabled). >> > > > > > > > >> > > > > > > > Regards, >> > > > > > > > Ben >> > > > > > > > >> > > > > > > >> > > > > > >> > > > > >> > > > >> > > >> > >> > >
